Textbooks

Çetinkaya-Rundel and Hardin, Introduction to Modern Statistics. https://openintro-ims.netlify.app/

Ismay & Kim, Modern Dive: Statistical Inference via Data Science. https://moderndive.com

Required Technology

R is the statistical software we will be using in this course (https://cran.r-project.org/)

RStudio is the most popular way to interact with the R software. We will be interacting with RStudio through RStudio Cloud (https://rstudio.cloud/). You will join the Stat 313 workspace, and then be able to access the course homework and lab assignments. We will be walking through this in the first week of lab!

For questions of general interest, such as course clarifications or conceptual questions, please use the Class Discord Server. Refer to the Day One Class Setup materials for more information on how to effectively use this server.

Welcoming Classroom

I value diversity, inclusion and equity in this (and every) class. I hold the fundamental belief that everyone is fully capable of learning and doing statistics. There is more than one way to address a statistical problem, and our learning will be richer by being open to different ideas, rejecting stereotypes, and being aware of—in order to minimize—our biases. I look forward to getting to know you all as individuals and as a learning community.

Course Description and Learning Objectives

Catalog Description: Applications of statistics for students not majoring in statistics or mathematics. Analysis of variance including one-way classification, randomized blocks, and factorial designs; multiple regression, model diagnostics, and model comparison. Prerequisite: Stat 217, Stat 218, Stat 221 or Stat 312

Expected Outcomes:

• Students will be able to identify, describe, and contrast different study designs:

  • observational studies
  • one-factor completely randomized designs
  • randomized complete block designs

• Analyze and interpret the results of analysis of variance (ANOVA) from different study designs, including multiple comparisons of means

• Analyze and interpret the results of simple linear regression models, and multiple linear regression models using both quantitative and categorical predictor variables.

• Understand and know how to use variable selection techniques in regression.

• Describe the assumptions of regression models and evaluate whether they are violated for a particular analysis.

• Describe Type I and Type II errors in the context of a study, and critically evaluate how different aspects of a study design will affect these errors

• Identify potential ethical issues surrounding data collection and analysis

• Create thoughtful single variable, two variable, and multivariable data visualizations to aid in the communication of statistical findings

• Carry out data wrangling tasks required to prepare data for analysis, including selecting variables, filtering observations, and mutating variables

• Understand and describe the importance of reproducibility in scientific research, and collaborate in groups to create a reproducible statistical report

Unit 1: Foundations of Statistics (Week 1)

This introductory unit has three big tasks, (1) review statistical and data oriented concepts you have (likely) seen before, (2) think critically about why statistics is used in science, and (3) think about how (historically) statistics has been used for inference.

Reading: Chapters 1 and 2 in Introduction to Modern Statistics (IMS), with supplementary articles

Unit 2: Exploratory Data Analysis (Weeks 2 & 3)

This unit focuses on building skills for working with and visualizing different types of data. First, we will focus on categorical data–creating summary tables and barcharts. Next, we will turn our attention to numerical data–calculating summary statistics, histograms, scatterplots, and linegraphs.

This unit will pair:

• Chapter 5 in IMS with Chapter 2 (sections 1-7) in Modern Dive
• Chapter 4 in IMS with Chapter 2 (section 8) in Modern Dive

Unit 3: Regression Modeling (Weeks 5 & 6)

In this unit we finally begin exploring statistical methods. You will put the tools you learned for wrangling and visualizing to work in the context of linear regression. We will start in a (likely) familiar context–linear regression. Once we’ve explored the concepts of “simple” / basic regression we will turn up the heat and add some additional explanatory variables with multiple linear regression.

This unit will pair:

• Chapter 7 in IMS with Chapter 5 in Modern Dive
• Chapter 8 in IMS with Chapter 6 in Modern Dive

Unit 4: Foundations of Statistical Inference (Weeks 7 & 8)

This unit will start with Chapter 7 in Modern Dive, setting the stage for why Statisticians care so much about variability. We will then use these ideas to walk through chapters 11 through 13 in Introduction to Modern Statistics, exploring different methods for summarizing the variability we might expect to see in different samples.

We will use these avenues to explore concepts you have seen before: hypothesis tests and confidence intervals. However, the main focus of these concepts will be on the idea of sampling variability not significance testing. We will visit the ideas of p-values and significance testing, with a emphasis on making (and justifying) sound scientific decisions.

Unit 5: Inference for Regression (Week 9)

Now that we’ve discussed the ideas behind sampling variability and statistical inference, we’ll explore these ideas in the context of linear regression. We will explore simple linear regression first, looking at how we can assess how “good” of a job our explanatory variable does in explaining the response variable. Next we’ll discuss how we can extend these ideas to a regression with multiple explanatory variables–using model selection criteria.

This unit will explore:

• Chapter 24 in IMS with Chapter 10 in Modern Dive
• Chapter 25 in IMS

Unit 6: ANOVA a (Boring) Case of Regression (Week 10)

To wrap up the quarter, we will look at a special case of linear regression–ANOVA. In this special case, our regression will include only categorical variables as explanatory variables. We will first review how we compare the means of two groups and then connect with what we learned about categorical variables in multiple linear to conceptualize how we can compare the means of three or more groups.

This unit will explore Chapter 21 and 22 in IMS

Communication

Every Sunday evening there will be an announcement on Canvas letting you know what is due over the next week, and the material we will be covering. The module for each week will be released on Sunday evening, so you can look over the content and see what the plan is for the week.

We will use Discord to manage questions and responses regarding course content. There are channels for the different components of each week (e.g., Week 1 Lab Assignment). Please do not send an email about homework questions or questions about the course material. It is incredibly helpful for others in the course to see the questions you have and the responses to those questions. I will try to answer any questions posted to Discord within 3-4 hours (unless it is posted at midnight). If you think you can answer another student’s question, please respond!

Generally, I will work either Saturday or Sunday each weekend, and will work from approximately 7am to 5pm during the week. I will attempt to respond to emails in 24 hours, but emails sent on a Saturday night may not be responded to until Monday morning. If you don’t hear back from me in 48 hours, assume I did not receive your email and resend it!

Individual Assignments

Team-based Assignments

Team Member Roles

Your team will be rotating group roles each week, so that one person does not act as the “team manager” for more than one week. Instead the following roles will circulate each week, so that each member of the group is able to complete each role.

There will be confidential peer evaluations completed every two weeks. I will use these to check-in on each group’s dynamics and ensure that everyone feels their voice is being heard.

Team Meetings

There will be time in each class for your team to work on the assignment for the week, however this time will not be sufficient to complete the assignments. Therefore, every group is expected to meet for at least 2-hours outside of class.

If you are not in attendance for more than one of your team meetings that week, you will be expected to complete that week’s assignment on your own.

My hope is that each member of the group looks over the week’s assignment on Monday while reading the week’s chapter(s). Then, each member should have some initial ideas to propose by Tuesday’s team meeting. During this meeting the reporter can begin the process of writing up the group’s ideas. The reporter will then provide the editor with their summary of the group’s ideas. As a team, you can choose to work together or independently on the assignment’s problems between course meetings. The manager is responsible for scheduling additional meetings, so that the editor is able to submit the assignment on time.

Late Work Policy

Assignments and redos are expected to be submitted on time. However, every student will be permitted to submit one individual assignment 24 hours late without question. You do not need to contact me to use this allowance, but if you find yourself in a position where you have used this allowance and you cannot complete another assignment by the due date, you are expected to email me. Once you email me, we can work together to find a deadline that is fair to both you and other students. If I do not hear from you, I will take a 5% reduction in score for every day an assignment is late, up to four days

You are also expected to participate in the week’s team collaborations in a timely manner. I will be able to monitor your involvement through your course attendance. If you find that you are unable to participate in the week’s team collaborations in a timely manner, please let your team know and contact me. Once you contact me, we can create a timeline that will both allow for you to engage in the assignment and allow for members of your team to receive feedback from you.

Behavioral Expectations

This course will use group work and classroom activities to better engage your understanding of the material. The environment for these discussions and activities is expected to adhere to ground rules, so that we create an open space where everyone feels welcome and supported, and comfortable with voicing their opinion.

By participating in this community, students accept to abide by the classroom Code of Conduct and accept the procedures by which any Code of Conduct incidents are resolved. Any form of behavior to exclude, intimidate, or cause discomfort is a violation of the Code of Conduct. In order to foster a positive and professional learning environment we encourage the following kinds of behaviors in all platforms and events:

• use welcoming and inclusive language
• everyone participates and no one dominates
• listen to understand
• share airtime
• one speaker at a time
• disagree with respect
• be respectful of different viewpoints and experiences
• all ideas are valid
• gracefully accept constructive criticism
• treat everything you hear as an opportunity to learn and grow
• seek common ground and understanding

Cheating and Plagiarism.

Simply put, I will not tolerate cheating or plagiarism.

Any incident of dishonesty, copying, exam cheating, or plagiarism will be reported to the Office of Student Rights and Responsibilities.

Cheating will earn you a grade of 0 on the assignment and an overall grade penalty of at least 10%. In circumstances of flagrant cheating, you may be given a grade of F in the course.

Paraphrasing or quoting another’s work without citing the source is a form of academic misconduct. This included the R code produced by someone else! Writing code is like writing a paper, it is obvious if you copied-and-pasted a sentence from someone else into your paper because the way each person writes is different.

Even inadvertent or unintentional misuse or appropriation of another’s work (such as relying heavily on source material that is not expressly acknowledged) is considered plagiarism. If you are struggling with writing the R code for an assignment, please reach out to me. I would prefer that I get to help you rather than you spending hours Googling things and get nowhere!

If you have any questions about using and citing sources, you are expected to ask for clarification.

For more information about what constitutes cheating and plagiarism, please see https://academicprograms.calpoly.edu/content/academicpolicies/Cheating.